Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Lecturer in Biomathematics







Huxley BuildingSouth Kensington Campus





I am a Lecturer (Assistant Professor with tenure) in Biomathematics at the Department of Mathematics at Imperial College London.

My research areas primarily include topological data analysis, algebraic statistics, and nonlinear algebra applied to mathematical and computational biology.

I earned my PhD from the Swiss Federal Institute of Technology in Lausanne (EPFL). Prior to joining Imperial, I held postdoctoral and visiting faculty positions at the Technion–Israel Institute of Technology, Duke University, Columbia University in the City of New York, and Tel Aviv University. 

More information about me and my professional activities, including available opportunities to work with me, can be found on my personal webpage; more detail on my research is available at the Research tab above.

Some Highlights:

I am a UK Research and Innovation Engineering and Physical Sciences Research Council (UKRI–EPSRC) Mathematical Sciences Early Career Forum member. As a forum member, I represent young mathematicians in the UK and advocate for our research needs, as well as promote the EPSRC in the mathematical sciences community in the UK.

I have raised over US $950'000 in research funding from internal, external, and international sources as PI and co-PI; I received three funding awards within my first year at Imperial.

I have had four proposals accepted to host 5-day workshops at the Banff International Research Station.

I was featured as a Nonlinear Algebra Researcher at the Max Planck Institute for Mathematics in the Sciences.

Selected Publications

Journal Articles

Lin B, Monod A, Yoshida R, 2020, Tropical Geometric Variation of Phylogenetic Tree Shapes, Discrete & Computational Geometry, ISSN:0179-5376

Lee W, Li W, Lin B, et al., 2019, Tropical Optimal Transport and Wasserstein Distances, Information Geometry, ISSN:2511-2481

Gartrell-Corrado RD, Chen AX, Rizk EM, et al., 2020, Linking transcriptomic and imaging data defines features of a favorable tumor immune microenvironment and identifies a combination biomarker for primary melanoma, Cancer Research, Vol:80, ISSN:0008-5472, Pages:1078-1087

Crawford L, Monod A, Chen AX, et al., 2019, Predicting clinical outcomes in Glioblastoma: an application of topological and functional data analysis, Journal of the American Statistical Association, Vol:115, ISSN:0162-1459, Pages:1139-1150

Monod A, Kališnik S, Patin͂o-Galindo JÁ, et al., 2019, Tropical sufficient statistics for persistent homology, Siam Journal on Applied Algebra and Geometry, Vol:3, ISSN:2470-6566, Pages:337-371

Gartrell RD, Marks DK, Hart TD, et al., 2018, Quantitative analysis of immune infiltrates in primary melanoma, Cancer Immunology Research, Vol:6, ISSN:2326-6066, Pages:481-493

Monod A, 2014, Random Effects Modeling and the Zero-Inflated Poisson Distribution, Communications in Statistics-Theory and Methods, Vol:43, ISSN:0361-0926, Pages:664-680


Monod A, Sigbeku J, Saucan E, 2022, Curved Markov Chain Monte Carlo for network learning, 0th International Conference on Complex Networks and their Applications, Springer Verlag, Pages:461-473, ISSN:1860-949X

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